Integration of single-cell multi-omics for gene regulatory network inference

Xinlin Hu, Yaohua Hu, Fanjie Wu, Ricky Wai Tak Leung, Jing Qin

Research output: Contribution to journalReview articlepeer-review

21 Citations (Scopus)

Abstract

The advancement of single-cell sequencing technology in recent years has provided an opportunity to reconstruct gene regulatory networks (GRNs) with the data from thousands of single cells in one sample. This uncovers regulatory interactions in cells and speeds up the discoveries of regulatory mechanisms in diseases and biological processes. Therefore, more methods have been proposed to reconstruct GRNs using single-cell sequencing data. In this review, we introduce technologies for sequencing single-cell genome, transcriptome, and epigenome. At the same time, we present an overview of current GRN reconstruction strategies utilizing different single-cell sequencing data. Bioinformatics tools were grouped by their input data type and mathematical principles for reader's convenience, and the fundamental mathematics inherent in each group will be discussed. Furthermore, the adaptabilities and limitations of these different methods will also be summarized and compared, with the hope to facilitate researchers recognizing the most suitable tools for them.

Original languageEnglish
Pages (from-to)1925-1938
Number of pages14
JournalComputational and Structural Biotechnology Journal
Volume18
DOIs
Publication statusPublished - 2020

Keywords

  • Gene regulatory network inference
  • Single-cell multi-omics integration
  • Single-cell sequencing

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